Designing and Implementing a Microsoft Azure AI Solution Exam Dumps

AI-102 Exam Format | Course Contents | Course Outline | Exam Syllabus | Exam Objectives

Exam Number: AI-102

Exam Name : Designing and Implementing a Microsoft Azure AI Solution



Exam TOPICS



The Cisco Customer Success Manager (DTCSM) v2.1 course gives you the confidence and competence to fulfill the Customer Success Manager (CSM) role successfully, helping your customers realize value from their solutions and achieve their business outcomes. The course offers experiential learning through practical exercises using situations based on real-life use cases and case studies. In this highly interactive course, you can practice and gain confidence in fulfilling core tasks using best-practice tools and methodologies while receiving feedback from the facilitator and your peers.



Candidates for the Azure AI Engineer Associate certification should have subject matter expertise building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.



Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, maintenance, performance tuning, and monitoring.



Azure AI Engineers work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, and AI developers to build complete end-to-end AI solutions.



Candidates for this certification should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure.



They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principle.



Plan and manage an Azure Cognitive Services solution

Implement Computer Vision solutions

Implement natural language processing solutions

Implement knowledge mining solutions

Implement conversational AI solutions



Plan and Manage an Azure Cognitive Services Solution (15-20%)

Select the appropriate Cognitive Services resource

 select the appropriate cognitive service for a vision solution

 select the appropriate cognitive service for a language analysis solution

 select the appropriate cognitive Service for a decision support solution

 select the appropriate cognitive service for a speech solution

Plan and configure security for a Cognitive Services solution

 manage Cognitive Services account keys

 manage authentication for a resource

 secure Cognitive Services by using Azure Virtual Network

 plan for a solution that meets responsible AI principles

Create a Cognitive Services resource

 create a Cognitive Services resource

 configure diagnostic logging for a Cognitive Services resource

 manage Cognitive Services costs

 monitor a cognitive service

 implement a privacy policy in Cognitive Services

Plan and implement Cognitive Services containers

 identify when to deploy to a container

 containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics,
Speech, Form Recognizer)

 deploy Cognitive Services Containers in Microsoft Azure

Implement Computer Vision Solutions (20-25%)

Analyze images by using the Computer Vision API

 retrieve image descriptions and tags by using the Computer Vision API

 identify landmarks and celebrities by using the Computer Vision API

 detect brands in images by using the Computer Vision API

 moderate content in images by using the Computer Vision API

 generate thumbnails by using the Computer Vision API

Extract text from images

 extract text from images or PDFs by using the Computer Vision service

 extract information using pre-built models in Form Recognizer

 build and optimize a custom model for Form Recognizer

Extract facial information from images

 detect faces in an image by using the Face API

 recognize faces in an image by using the Face API

 analyze facial attributes by using the Face API

 match similar faces by using the Face API

Implement image classification by using the Custom Vision service

 label images by using the Computer Vision Portal

 train a custom image classification model in the Custom Vision Portal

 train a custom image classification model by using the SDK

 manage model iterations

 evaluate classification model metrics

 publish a trained iteration of a model

 export a model in an appropriate format for a specific target

 consume a classification model from a client application

 deploy image classification custom models to containers

Implement an object detection solution by using the Custom Vision service

 label images with bounding boxes by using the Computer Vision Portal

 train a custom object detection model by using the Custom Vision Portal

 train a custom object detection model by using the SDK

 manage model iterations

 evaluate object detection model metrics

 publish a trained iteration of a model

 consume an object detection model from a client application

 deploy custom object detection models to containers

Analyze video by using Video Indexer

 process a video

 extract insights from a video

 moderate content in a video

 customize the Brands model used by Video Indexer

 customize the Language model used by Video Indexer by using the Custom Speech service

 customize the Person model used by Video Indexer

 extract insights from a live stream of video data

Implement Natural Language Processing Solutions (20-25%)

Analyze text by using the Text Analytics service

 retrieve and process key phrases

 retrieve and process entity information (people, places, urls, etc.)

 retrieve and process sentiment

 detect the language used in text

Manage speech by using the Speech service

 implement text-to-speech

 customize text-to-speech

 implement speech-to-text

 improve speech-to-text accuracy

 improve text-to-speech accuracy

 implement intent recognition

Translate language

 translate text by using the Translator service

 translate speech-to-speech by using the Speech service

 translate speech-to-text by using the Speech service

Build an initial language model by using Language Understanding Service (LUIS)

 create intents and entities based on a schema, and then add utterances

 create complex hierarchical entities

o use this instead of roles

 train and deploy a model

Iterate on and optimize a language model by using LUIS

 implement phrase lists

 implement a model as a feature (i.e. prebuilt entities)

 manage punctuation and diacritics

 implement active learning

 monitor and correct data imbalances

 implement patterns

Manage a LUIS model

 manage collaborators

 manage versioning

 publish a model through the portal or in a container

 export a LUIS package

 deploy a LUIS package to a container

 integrate Bot Framework (LUDown) to run outside of the LUIS portal

Implement Knowledge Mining Solutions (15-20%)

Implement a Cognitive Search solution

 create data sources

 define an index

 create and run an indexer

 query an index

 configure an index to support autocomplete and autosuggest

 boost results based on relevance

 implement synonyms

Implement an enrichment pipeline

 attach a Cognitive Services account to a skillset

 select and include built-in skills for documents

 implement custom skills and include them in a skillset

Implement a knowledge store

 define file projections

 define object projections

 define table projections

 query projections

Manage a Cognitive Search solution

 provision Cognitive Search

 configure security for Cognitive Search

 configure scalability for Cognitive Search

Manage indexing

 manage re-indexing

 rebuild indexes

 schedule indexing

 monitor indexing

 implement incremental indexing

 manage concurrency

 push data to an index

 troubleshoot indexing for a pipeline

Implement Conversational AI Solutions (15-20%)

Create a knowledge base by using QnA Maker

 create a QnA Maker service

 create a knowledge base

 import a knowledge base

 train and test a knowledge base

 publish a knowledge base

 create a multi-turn conversation

 add alternate phrasing

 add chit-chat to a knowledge base

 export a knowledge base

 add active learning to a knowledge base

 manage collaborators

Design and implement conversation flow

 design conversation logic for a bot

 create and evaluate *.chat file conversations by using the Bot Framework Emulator

 choose an appropriate conversational model for a bot, including activity handlers and dialogs

Create a bot by using the Bot Framework SDK

 use the Bot Framework SDK to create a bot from a template

 implement activity handlers and dialogs

 use Turn Context

 test a bot using the Bot Framework Emulator

 deploy a bot to Azure

Create a bot by using the Bot Framework Composer

 implement dialogs

 maintain state

 implement logging for a bot conversation

 implement prompts for user input

 troubleshoot a conversational bot

 test a bot

 publish a bot

 add language generation for a response

 design and implement adaptive cards

Integrate Cognitive Services into a bot

 integrate a QnA Maker service

 integrate a LUIS service

 integrate a Speech service

 integrate Dispatch for multiple language models

 manage keys in app settings file

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Microsoft
AI-102
Designing and Implementing a Microsoft Azure AI Solution
http://killexams.com/pass4sure/exam-detail/AI-102
Question: 116
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 117
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 118
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 119
You build a custom Form Recognizer model.
You receive sample files to use for training the model as shown in the following table.
Which three files can you use to train the model? Each correct answer presents a complete solution. (Choose three.)
NOTE: Each correct selection is worth one point.
A. File1
B. File2
C. File3
D. File4
E. File5
F. File6
Answer: ACF
Explanation:
Input requirements
Form Recognizer works on input documents that meet these requirements:
Format must be JPG, PNG, PDF (text or scanned), or TIFF. Text-embedded PDFs are best because there�s no
possibility of error in character extraction and location. File size must be less than 50 MB.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer/overview
Question: 120
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 121
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 122
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 123
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 124
DRAG DROP
You plan to use a Language Understanding application named app1 that is deployed to a container.
App1 was developed by using a Language Understanding authoring resource named lu1.
App1 has the versions shown in the following table.
You need to create a container that uses the latest deployable version of app1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
Step 1: Export the model using the Export for containers (GZIP) option.
Export versioned app�s package from LUIS portal
The versioned app�s package is available from the Versions list page.
Question: 125
DRAG DROP
You plan to use containerized versions of the Anomaly Detector API on local devices for testing and in on-premises
datacenters.
You need to ensure that the containerized deployments meet the following requirements:
� Prevent billing and API information from being stored in the command-line histories of the devices that run the
container.
� Control access to the container images by using Azure role-based access control (Azure RBAC).
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions
to the answer area and arrange them in the correct order. (Choose four.)
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you
select.
Answer:
Explanation:
Step 1: Pull the Anomaly Detector container image.
Step 2: Create a custom Dockerfile
Step 3: Push the image to an Azure container registry.
To push an image to an Azure Container registry, you must first have an image.
Step 4: Distribute the docker run script
Use the docker run command to run the containers.
Reference: https://docs.microsoft.com/en-us/azure/container-registry/container-registry-intro
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AI-102 Designing PDF Dumps

AI and Design

constructing human-founded, physics-primarily based, and AI-enabled design methods for creating engineering products and techniques that handle societal challenges

Design combines engineering, company, the arts, and the social sciences to tackle advanced societal challenges. Designers use laptop gaining knowledge of and synthetic intelligence (AI) to work on the interface between know-how and society. In collaboration with the Segal Design Institute and the core for Human-desktop interplay+Design, we advance human-centered design concepts, collective-innovation platforms, and AI-enabled computational design methods.

View AI and design faculty

technology has infiltrated every element of our lives from how we work and gain knowledge of to how we play and socialize. We blend engineering and social sciences to construct novel socio-technical methods to increase human conduct and strengthen theories of society and expertise interactions. We believe the context by which the techniques are deployed and the human cognition, emotion, and behavior and the ways by which the know-how can aid, complement, or increase human skill.

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affiliate Professor of desktop Science

associate Professor of Mechanical Engineering

associate Professor of physical medicine and Rehabilitation

e mail Brenna Argall

Cardiss Collins Professor of Mechanical Engineering and (via courtesy) Civil and Environmental Engineering and materials Science and Engineering

Director, Northwestern Initiative for Manufacturing Science and Innovation (NIMSI)

email Jian Cao

James N. and Nancy J. Farley Professor in Manufacturing & Entrepreneurship

Director, Theoretical and applied Mechanics application

Director of the Institute for mobile Engineering technologies

Professor of Mechanical Engineering and (through courtesy) Biomedical Engineering and Civil and Environmental Engineering

electronic mail Horacio Espinosa

Professor of Mechanical Engineering and (with the aid of courtesy) computing device Science

Professor of verbal exchange studies

Co-Director, middle for Human computer interaction + Design

electronic mail Elizabeth Gerber

Professor and affiliate Chair of Mechanical Engineering and Civil and Environmental Engineering and (by way of courtesy) Biomedical Engineering

June and Donald Brewer Professor

email Sinan Keten

Walter P. Murphy Professor of Mechanical Engineering & Civil and Environmental Engineering and (via courtesy) materials Science and Engineering

electronic mail Wing Liu

associate Professor of Chemical and biological Engineering and (by using courtesy) Mechanical Engineering

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associate Director, Northwestern-Argonne Institute of Science and Engineering

email Jennifer Dunn

Walter P. Murphy Professor of Chemical and organic Engineering and (with the aid of courtesy) Mechanical Engineering

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References

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